Forecasting photovoltaic power using bagging feed-forward neural network
This paper presents a forecast model of the active power of a photovoltaic (PV) power generation system. In this model, a feed-forward neural network (FNN) is combined with bootstrap aggregation techniques using the Box–Cox transformation, seasonal and trend decomposition using Loess, and a moving b...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/345501 |
| Acceso en línea: | https://hdl.handle.net/2117/345501 |
| Access Level: | acceso abierto |
| Palabra clave: | Photovoltaic power generation Bootstrap Demand forecasting Energy consumption Feed-forward neural networks Photovoltaic systems Energia solar fotovoltaica Àrees temàtiques de la UPC::Energies::Energia solar tèrmica |
| Sumario: | This paper presents a forecast model of the active power of a photovoltaic (PV) power generation system. In this model, a feed-forward neural network (FNN) is combined with bootstrap aggregation techniques using the Box–Cox transformation, seasonal and trend decomposition using Loess, and a moving block bootstrap (MBB) technique. An analysis is conducted using the data provided by the active power of the PV power generation system; the data are collected every 30 min for 12 months. The FNN method combined with MBB techniques consistently outperformed the original FNN in terms of forecasting accuracy based on the root mean squared error, on the forecast from one day of anticipation. The results are statistically significant as demonstrated through the Ljung–Box test, which verifies that the forecast errors are not correlated, thereby validating the proposed model. |
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